How Managers Hire Great Engineers [Part 23] - Building Relationships

Social capital is incredibly important when hiring. From this lesson, you will:

- Learn about the importance of building relationships in recruitment, similar to how VCs generate deal flow
- Discover the strategy used by Benjamin Nazad, who leveraged academic directories to network with potential contacts in the VC industry
- Understand that cultivating relationships with each candidate can significantly enhance the likelihood of closing them during the hiring process
- Recognize the long-term value of maintaining relationships even when there are no immediate job openings, facilitating quicker recruitment when positions become available

Connect with John Egan on LinkedIn: <https://www.linkedin.com/in/jwegan/>

Taro Book Club: Designing Data Intensive Applications - Chapter 5 (Replication)

Topic: Chapter 5 - Replication

Session led by: [Rohit Katyal](https://www.linkedin.com/in/rohitkatyal/)

In this #taro-bookclub discussion, we'll be talking about Chapter 5 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- Let’s get to know each other with some quick introductions.
- We’ll break down and discuss the key concepts from Chapter 5.
- There will also be time for any questions and answers you might have.

Please send your questions ahead of time to [**Rohit Katyal**](https://www.linkedin.com/in/rohitkatyal/) in the [**Taro Premium**](https://www.jointaro.com/membership/) Slack, so that they can be included in the discussion.

Get ready for another engaging and informative session!

The Effect of Interest Rates

We walk through how macroeconomic conditions—especially interest rates—directly affect tech companies and tech workers.

- The federal funds rate guides how expensive it is for banks (and ultimately companies) to borrow money, and while near-zero rates felt normal recently, historically higher interest rates are actually the norm.
- When interest rates rise, the cost of borrowing increases, which makes companies far less willing to invest in long-term bets that may not pay off for several years.
- Because the tech industry is heavily driven by growth funded through borrowing and investment, higher rates trigger spending pullbacks, reduced hiring, and ultimately the contraction and layoffs we’re seeing across the market.

The Case FOR Big Tech

We outline scenarios where Big Tech can be a highly advantageous career move—especially for early-career engineers or those seeking financial security and stability.

- **Ideal for Early-Career Engineers**: Big Tech provides a strong engineering foundation for those just out of college or with limited experience. Newer engineers are more adaptable to the culture and expectations.
- **Higher Workload Tolerance**: Younger professionals often have the energy and flexibility to handle the demanding pace and longer hours that come with Big Tech roles.
- **Minimal Habits to Unlearn**: Junior engineers aren’t “programmed” with habits from less rigorous environments, making it easier for them to succeed in fast-paced, high-standard teams.
- **Excellent Compensation**: For those with high financial needs—whether due to family obligations or living in expensive cities—Big Tech offers some of the most competitive compensation packages, including equity.
- **Job Stability**: These companies offer consistent employment for high performers, making them a good choice for those with lower risk tolerance who prefer predictability.

The How of the Engineering Career Ladder: Five Essential Foundations for Advancement

Session led by [Shine Garg](https://www.linkedin.com/in/shinegarg/)

Engineering career ladders lay out the “what” of the software engineering career path—what competencies and behaviors are needed at each milestone. However, they often lack guidance on the “how.”

Understanding how—and how quickly—you can acquire and exhibit these competencies significantly impacts your career trajectory, financial future, and sense of fulfillment in life.

This talk demystifies the process of climbing the engineering career ladder. Join us to learn the five essential foundations for advancing your career and achieving your goals.

Algorithms You Must Master

In this section, we get a clear roadmap of the **core algorithms** we need to master for ML algorithm coding interviews. These are foundational models that balance simplicity with depth—perfect for testing both theory and implementation skills under time pressure.

- For **clustering**, we should be able to implement **k-means from scratch**, including centroid initialization, assignment, recomputation, and convergence checks. We should also understand evaluation metrics like inertia and silhouette score.
- For **linear models**, we must code **linear and logistic regression** with gradient descent, using proper loss functions and optionally adding **L1/L2 regularization**.
- For **tree-based methods**, we should implement a **basic decision tree**, and if time allows, understand how **random forests** build on it using bagging and bootstrapping.
- In **k-nearest neighbors**, we focus on **distance calculations** (Euclidean vs. cosine) and how to retrieve the top-k neighbors for a query point.
- Lastly, for **neural networks**, we should know how to build a **simple feedforward network** with one or two hidden layers and train it using **backpropagation**, including activation functions like ReLU and sigmoid, along with their gradients.

By preparing these specific algorithms, we’ll be equipped to handle most ML algorithm coding interviews with confidence and precision.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: [**https://calendly.com/your-friend-yayun/1on1**](https://calendly.com/your-friend-yayun/1on1)
- Follow her on LinkedIn: [**https://www.linkedin.com/in/yayunjin/**](https://www.linkedin.com/in/yayunjin/)

When Should I Actually Use AI During The Interview?

In this lesson we talk about when and how to use AI effectively during an AI-assisted coding interview.

- **Find the right balance with AI:** Learn how to avoid over-delegating core logic to AI or underusing it and running out of time, by aiming for the “middle path” that maximizes leverage while minimizing risk.
- **Accelerate typing, not thinking:** Discover why you should use AI for scaffolding, boilerplate, and clearly defined test cases—but retain ownership over algorithms, data structures, and key design decisions.
- **Optimize for easy verification:** Understand that AI is most helpful when you already know what needs to be built and can quickly verify the output, and least helpful when you’re still figuring out the correct approach.

Maximize Your Equity And Benefits In Tech [Part 18] - Liquidity And Workarounds

Accessing retirement funds early can be tricky, but there are some strategic exceptions. Here’s the core points from the lesson:

- Withdrawing from 401(k)s or pre-tax IRAs before age 59.5 typically triggers a 10% penalty, though exceptions exist for things like first-time home purchases, disability, or high medical costs
- Roth IRA contributions (not earnings or conversions) can be withdrawn at any time, penalty- and tax-free, while Roth conversions require a five-year wait before penalty-free access
- Strategies like the Roth conversion ladder or 72(t) withdrawals (Substantially Equal Periodic Payments) can help bridge the gap if retiring before 59.5
- If you leave your job at 55 or older, you can take penalty-free withdrawals from that employer’s 401(k), and possibly from other plans rolled into it

Next steps with Andy:

- [Connect with Andy on LinkedIn](https://www.linkedin.com/in/andymoranio)
- [Leave feedback and/or sign up for Andy’s newsletter](https://subscribepage.io/taroequitycourse)
- [Schedule a consultation](http://adastrafp.com/contact-us/)

Ship & Grow 500k+ User Side Projects [Part 13] - Observe Your Users

If you're at this point in the course, you now deeply understand why feedback is great. However, the vast majority of users simply won't provide you with feedback. This lesson teaches you:

- How most end-users will behave with your side project (and any product really)
- What you should do proactively to extract signal from as many users as possible
- Important metrics to track when observing your users
- A great free tool you can use to see exactly how users are interfacing with your product

You can try PostHog here: <https://posthog.com/>

Amazon Behavioral Interview [Part 18] - Handling Unclear Expectations From Leadership (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about navigating conflicting priorities.

- **Strengths:**

  - **Acknowledged the Challenge:** The interviewee effectively described the challenging situation of balancing conflicting demands from leadership: driving customer satisfaction while maintaining employee engagement.
  - **Highlighted Key Actions:** They mentioned implementing strategies to improve customer satisfaction, such as focusing on key metrics like CSAT and time to resolution.
  - **Acknowledged Limitations:** The interviewee honestly acknowledged the limitations of their efforts, such as the loss of a valuable employee due to the challenging work environment.

- **Areas for Improvement:**

  - **Focus on Employee Engagement:** While the interviewee mentioned some efforts to improve employee morale, such as virtual happy hours, they could have further emphasized their proactive steps to address employee concerns and improve engagement.
    - This could include:
      - Implementing regular feedback mechanisms (e.g., surveys, one-on-one meetings).
      - Advocating for employee well-being initiatives.
      - Proactively addressing employee concerns and seeking solutions.
  - **Quantify Impact:** Instead of stating that they were "semi-successful," the interviewee could have quantified their impact by:
    - Describing specific improvements in employee engagement scores.
    - Highlighting successful initiatives that improved both customer satisfaction and employee morale.
  - **Demonstrate Proactive Problem-Solving:** The interviewee could have further emphasized their proactive problem-solving skills by:
    - Describing how they identified the root cause of the conflicting demands.
    - Explaining how they developed and implemented innovative solutions to address the challenges.
    - Highlighting how their actions led to positive and lasting change within the team and organization.

- **Overall:** The interviewee provided a solid foundation for their response. By incorporating the suggested improvements, they can further enhance their ability to effectively communicate their accomplishments and demonstrate their leadership, problem-solving, and results-oriented approach in a job interview setting.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Why Engineering Manager Interviews Are Hard

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

Many engineers find EM interviews difficult because they lack direct experience with the interview process, often having been promoted internally or being out of practice. The expectations for EM roles center around leadership, strategy, and stakeholder management rather than technical execution.

- **Limited Interview Experience:** Most engineering managers either transition through internal promotions or haven't interviewed for years, leading to a lack of familiarity with the process.
- **Different Decision-Makers and Focus:** Unlike IC roles, EM interview decisions are made by senior leadership who prioritize business strategy, people management, and stakeholder alignment over technical details.
- **Behavioral Focus:** Behavioral rounds dominate the EM interview process (about 80%) because they assess how candidates handle adversity and complex interpersonal dynamics.
- **No “LeetCode” for EM Interviews:** The subjective nature of these interviews makes it difficult to prepare using standard methods—there’s no one-size-fits-all approach to practicing answers.
- **Feedback is Rare and Vague:** Candidates often don't receive clear rejection reasons, leaving them uncertain about what to improve or how well their responses aligned with expectations.

Lack of Control

Engineers at Big Tech often experience a deep lack of control over their work, compensation, and long-term impact. Here are the core points from the lesson:

- Politics and top-down decision making mean engineers can invest heavily in projects only to have leadership reject ideas or directions with no recourse
- Compensation elements like company multipliers are tied to overall corporate performance, which individual engineers have virtually no influence over
- Constant reorgs, shifting org charts, and manager reshuffling create instability and make teams feel like they’re moving for reasons that aren’t always merit-based
- Big Tech frequently kills projects, even promising or widely used ones, because they don’t meet multibillion-dollar expectations, leaving engineers demoralized and powerless over product fate

ML System Design Framework

In this segment, we’re introduced to a powerful **5-part framework** for approaching ML system design interviews. This structured method ensures we address both technical depth and strategic thinking—key traits interviewers look for in strong candidates.

- **1. Problem Navigation**: We start by asking clarifying questions to understand functional and non-functional requirements, product goals, infrastructure constraints, and user experience. We define success metrics, state our assumptions, and frame the scope clearly.
- **2. High-Level Design**: We map business goals to ML objectives, walk through core system components (data ingestion, processing, modeling, inference, monitoring), and identify what needs to run in real-time versus batch.
- **3. Data Strategy & Feature Engineering**: We explain how data is collected and labeled, ensure data quality, and outline our approach to creating, storing, and updating features—including how we handle feature freshness and online/offline processing.
- **4. Modeling Approach**: We present specific model choices with trade-offs in interpretability, scalability, and accuracy. We detail training methods (batch, online, hybrid), address data distribution shifts, and outline infrastructure needs.
- **5. Evaluation, Deployment & Monitoring**: We describe offline evaluation strategies, deployment infrastructure (latency, model size, caching), and our plan for monitoring model health (drift, degradation) while tying results back to business KPIs.

Using this framework helps us think like **system-level ML engineers**, ensuring we cover everything from high-level goals to production-readiness with clarity and confidence.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Taro Book Club: Designing Data Intensive Applications - Chapter 6 (Partitioning)

Topic: Chapter 6 - Replication

Session led by: [Bhavana Gannu](https://www.linkedin.com/in/bhavanagannu/) and [Suraj Singh](https://www.linkedin.com/in/suraj-singh-495b192b/)

In this #taro-bookclub discussion, we talked about Chapter 6 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Ship & Grow 500k+ User Side Projects [Part 16] - Word Of Mouth

The most organic source of traffic is happy users simply telling their network how awesome your product is. This lesson teaches you:

- The main metric to understand when it comes to your word of mouth
- 2 product mechanisms you can add to juice up your word of mouth
- How to make an effective sharing flow, explained with an in-depth example
- How to make a compelling referral flow, shown with famous examples

Airbnb blog post about how they sharply refined their sharing flow (an oldie but a goodie): <https://medium.com/airbnb-engineering/growth-at-scale-getting-to-product-sharing-fit-ccb4b501ecf>

Feature Engineering

In this part of the course, we focus on **feature engineering**, a critical area where interviewers differentiate between average and exceptional ML candidates. This is our opportunity to show not just technical skill, but creativity and judgment.

- **Mediocre feature engineering** relies on basic, mechanical steps—like one-hot encoding categoricals or normalizing numericals—without much customization.
- **Stellar candidates go further**, creating new features from raw data (e.g., extracting time-of-day from timestamps, or combining variables into meaningful interactions).
- We show thoughtfulness by **dropping irrelevant features**, checking for **multicollinearity**, and aligning features closely with the problem's objectives.
- We demonstrate versatility by handling **complex data types**—such as transforming freeform text, working with embeddings, or aggregating time series effectively.
- Ultimately, we recognize that **feature engineering can matter more than the model itself**, and our job is to shape the data so that the model can truly learn from it.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Become An Engineer Influencer [Part 8] - Bottom-Funnel Channels

Ethical Concerns

Working at Big Tech can expose engineers to ethical dilemmas and reputational challenges, especially as these companies grow in size and societal influence. Here are the core points from the lesson:

- **Ethical baggage and public scrutiny** - Every large tech company accumulates controversy over time, from privacy scandals to monopolistic behavior. Employees often face external judgment and may feel conflicted about the company’s broader impact, such as gentrification and social displacement caused by high salaries and rapid expansion.
- **Disconnect between purpose and individual contribution** - Engineers may feel demoralized realizing their role (e.g., being engineer #80,532 at Google) probably won't meaningfully contribute to making the world a better place - especially for those motivated by social impact or purpose-driven work.
- **Reputation can shift dramatically** - Facebook was once viewed positively (e.g., in the late 2000s), but public perception reversed dramatically after scandals like Cambridge Analytica. Engineers went from proudly wearing company swag to avoiding brand affiliation in public. Similar sentiment shifts can hit any big company, even well-intentioned ones.
- **Moral complexity of innovation and market power** - As companies grow in influence and value, their innovations may disrupt other industries, leading to job loss or social backlash. While sometimes justified in capitalist terms, the ethical line becomes murkier when dominance is maintained through questionable means.

Amazon Behavioral Interview [Part 19] - Taking An Unpopular Stance (Answer)

This video segment describes a situation where the interviewee took an unpopular stance in a meeting with leadership.

- **Identified the Conflict:** The interviewee disagreed with the company's decision to significantly shift resources from the US to India for customer support, primarily driven by cost-cutting measures.
- **Presented an Alternative Solution:** Despite the potential for backlash, the interviewee proposed an alternative solution:
  - While acknowledging the cost-effectiveness of hiring in India, they suggested leveraging the "Year Up" program to hire underrepresented talent in the US, aligning with the company's diversity and inclusion goals.
- **Faced Opposition:** The interviewee faced strong opposition from the CFO, who was primarily focused on cost-cutting measures and reacted negatively to the alternative proposal.
- **Continued to Advocate:** Despite the initial setback and lack of immediate recognition, the interviewee continued to advocate for the inclusion of the "Year Up" program.
- **Achieved Partial Success:** While the initial proposal was largely dismissed, the company eventually implemented a limited version of the "Year Up" program, demonstrating the long-term impact of the interviewee's advocacy.

This example highlights the interviewee's: ***Proactive Approach:*** *They identified a potential solution that aligned with company values.* **Courage and Persistence:** They were willing to express their dissenting opinion despite potential negative consequences. ***Focus on Long-Term Impact:*** *They demonstrated a commitment to long-term goals, such as diversity and inclusion, even when faced with immediate pressures.* **Ability to Learn and Adapt:** They acknowledged the limitations of their initial approach and demonstrated a willingness to adapt and find alternative solutions.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

How Managers Hire Great Engineers [Part 19] - Speed

Great engineers don't stay on the market for a long time. From this lesson, you will:

- Understand that reducing the time from initial contact to offer acceptance can significantly improve your chances of closing a candidate
- Learn that a faster process can position your company advantageously against competitors who may have longer hiring cycles
- Discover how streamlining your recruitment process to make decisions quicker can prevent candidates from pursuing or waiting for other offers

Connect with John Egan on LinkedIn: <https://www.linkedin.com/in/jwegan/>

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.